main

app.py

@@ -1,47 +1,41 @@
+import gradio as gr
 from transformers import pipeline
 from PIL import Image
-import gradio as gr
 
+# ---- Load models ----
+text_classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
 
+image_classifier = pipeline("image-classification", model="microsoft/resnet-50")
 
-# Sentiment / text classification
-text_classifier = pipeline(
-    "text-classification",
-    model="distilbert-base-uncased-finetuned-sst-2-english" 
-)
-# Fake news detection
-fake_detector = pipeline(
-    "text-classification",
-    model="mrm8488/bert-tiny-finetuned-fake-news-detection"  # public
-)
-
-image_classifier = pipeline(
-    "image-classification",
-    model="microsoft/resnet-50"  # public
-)
+# audio_classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-ks")
 
+# Leaf disease detection (public model)
 leaf_classifier = pipeline(
     "image-classification",
-    model="linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification"  # public
+    model="nateraw/plant-disease-classification"
 )
 
-fruitveg_classifier = pipeline(
-    "image-classification",
-    model="Schram03/fruits-classification"  # public
-)
+
+fruitveg_classifier = pipeline("image-classification", model="mrdbourke/mobilenet_v2_fruit")
+
 
 
+# ---- Define functions ----
 def classify_text(text):
     result = text_classifier(text)[0]
     return {result['label']: float(result['score'])}
 
+def classify_image(image):
+    result = image_classifier(image)[0]
+    return {result['label']: float(result['score'])}
+
 def detect_fake(text):
     result = fake_detector(text)[0]
     return {result['label']: float(result['score'])}
 
-def classify_image(image):
-    result = image_classifier(image)[0]
-    return {result['label']: float(result['score'])}
+# def classify_audio(audio):
+#     result = audio_classifier(audio)[0]
+#     return {result['label']: float(result['score'])}
 
 def detect_leaf_disease(image):
     result = leaf_classifier(image)[0]
@@ -51,27 +45,47 @@ def detect_fruitveg(image):
     result = fruitveg_classifier(image)[0]
     return {result['label']: float(result['score'])}
 
+    
+# ---- Build Gradio interface ----
 with gr.Blocks() as demo:
-    with gr.Tab("Text Classification"):
-        gr.Interface(fn=classify_text, inputs="text", outputs="label")
-    with gr.Tab("Fake News Detection"):
-        gr.Interface(fn=detect_fake, inputs="text", outputs="label")
-    with gr.Tab("General Image Classification"):
-        gr.Interface(fn=classify_image, inputs=gr.Image(type="pil"), outputs="label")
-    with gr.Tab("Leaf Disease Detection"):
-        gr.Interface(fn=detect_leaf_disease, inputs=gr.Image(type="pil"), outputs="label")
-    with gr.Tab("Fruit/Veg Detection"):
-        gr.Interface(fn=detect_fruitveg, inputs=gr.Image(type="pil"), outputs="label")
-# def my_function(text): 
-#     return "You entered: " + text
-
-# demo = gr.Interface(fn=my_function, inputs="text", outputs="text")
-# demo = gr.Interface(fn=classify_image, inputs=gr.Image(type="pil"), outputs="label")
-demo.launch()
+    gr.Markdown("# 🧩 Multi-Model v1.0\nSupports **Text & Image Classification**")
+
+    with gr.Tab("Text Classifier"):
+        txt_input = gr.Textbox(label="Enter text")
+        txt_output = gr.Label()
+        txt_button = gr.Button("Classify Text")
+        txt_button.click(fn=classify_text, inputs=txt_input, outputs=txt_output)
+
+    with gr.Tab("Image Classifier"):
+        img_input = gr.Image(type="pil", label="Upload image")
+        img_output = gr.Label()
+        img_button = gr.Button("Classify Image")
+        img_button.click(fn=classify_image, inputs=img_input, outputs=img_output)
+
+     # with gr.Tab("Audio Classifier"):
+     #    aud_input = gr.Audio(sources=["microphone", "upload"], type="filepath", label="Record or Upload Audio")
+     #    aud_output = gr.Label()
+     #    aud_button = gr.Button("Classify Aut, outputs=aud_dio")
+     #    aud_button.click(fn=classify_audio, inputs=aud_inpuoutput)
+
+    with gr.Tab("Fake News Detector"):
+        fake_input = gr.Textbox(label="Enter news/article text")
+        fake_output = gr.Label()
+        fake_button = gr.Button("Detect Fake")
+        fake_button.click(fn=detect_fake, inputs=fake_input, outputs=fake_output)
+
+    with gr.Tab("Leaf Disease Detector"):
+        leaf_input = gr.Image(type="pil", label="Upload leaf image")
+        leaf_output = gr.Label()
+        leaf_button = gr.Button("Detect Disease")
+        leaf_button.click(fn=detect_leaf_disease, inputs=leaf_input, outputs=leaf_output)
+    
+    with gr.Tab("Fruit & Veg Detector"):
+        fv_input = gr.Image(type="pil", label="Upload fruit/vegetable image")
+        fv_output = gr.Label()
+        fv_button = gr.Button("Detect")
+        fv_button.click(fn=detect_fruitveg, inputs=fv_input, outputs=fv_output)
 
-def classify_and_detect(text):
-    t_result = classify_text(text)
-    f_result = detect_fake(text)
-    return t_result, f_result
 
-gr.Interface(fn=classify_and_detect, inputs="text", outputs=["label", "label"])
+
+demo.launch()

requirements.txt

@@ -1,5 +1,10 @@
-gradio
-transformers
 torch
-Pillow
+torchvision
+transformers
+fastapi
+uvicorn[standard]
+aiofiles
+python-multipart
+pillow
 soundfile
+gradio

resnet18_cifar10.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6829b35ffa842ed3eaa733182d8226ece82f77c052b75c802242318d735f9896
+size 11402991

resnet18_quantized.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:54868efdd06d073148f5f06a49bff601bded49386a4b83d312001ca71338193a
+size 11403367